## 設定
verbose = False

### 言語の割合の均等化
balanced = True

### LDA 用

## トピック数
n_topics = 15 # 30は多過ぎる？

## doc, term の設定
doc_type      = 'form'
doc_attr      = 'spell'
max_doc_size  = 12
##
term_size = 'character'
term_type = '2gram'
## skippy n-gram の結合範囲
max_distance_val = round(max_doc_size * 0.8)
print(f"max_distance_val: {max_distance_val}")

## ngram を包括的にするかどうか
ngram_is_inclusive = True

### DTM 構築

## term の最低頻度
term_min_freq = 2

## 高頻度 term の濫用指標: 大きくし過ぎないように．0.05 は十分に大きい
term_abuse_threshold = 0.05

max_distance_val: 10

import sys, os, random, re, glob
import pandas as pd
import pprint as pp
from functools import reduce

## load data to process
from pathlib import Path
import pprint as pp

wd = Path(".")
##
dirs = [ x for x in wd.iterdir() if x.is_dir() and not x.match(r"plot*") ]
if verbose:
    print(f"The following {len(dirs)} directories are potential targets:")
    pp.pprint(dirs)

## list up files in target directory 
wd = Path(".")
target_dir = "data-words" # can be changed
target_files = sorted(list(wd.glob(f"{target_dir}/*.csv")))
#
print(f"\n{target_dir} contains {len(target_files)} files to process")
pp.pprint(target_files)

data-words contains 9 files to process
[PosixPath('data-words/base-sound-English-r6e-originals.csv'),
 PosixPath('data-words/base-sound-German-r1a-original.csv'),
 PosixPath('data-words/base-spell-English-r6e-originals.csv'),
 PosixPath('data-words/base-spell-Esperanto-r0-orginal.csv'),
 PosixPath('data-words/base-spell-French-r0-originals.csv'),
 PosixPath('data-words/base-spell-German-r1a-originals.csv'),
 PosixPath('data-words/base-spell-Icelandic-r0-original.csv'),
 PosixPath('data-words/base-spell-Russian-r0-originals.csv'),
 PosixPath('data-words/base-spell-Swahili-r0-orginal.csv')]

import pandas as pd

## データ型の辞書
types = "spell sound freq".split(" ")
type_setting = { t : 0 for t in types }
print(type_setting)

## 言語名の辞書
langs = "english esperanto french german icelandic russian swahili".split(" ")
#langs = "english esperanto french german russian swahili".split(" ")
#langs = "english esperanto french german icelandic swahili".split(" ")
lang_setting = { lang : 0 for lang in langs }
print(lang_setting)

## 辞書と統合
settings = { 'form': None, **type_setting, **lang_setting }
print(settings)

{'spell': 0, 'sound': 0, 'freq': 0}
{'english': 0, 'esperanto': 0, 'french': 0, 'german': 0, 'icelandic': 0, 'russian': 0, 'swahili': 0}
{'form': None, 'spell': 0, 'sound': 0, 'freq': 0, 'english': 0, 'esperanto': 0, 'french': 0, 'german': 0, 'icelandic': 0, 'russian': 0, 'swahili': 0}

vars = list(settings.keys())
print(f"targe var names: {vars}")

d_parts = [ ]
for lang in langs:
    local_settings = settings.copy()
    print(f"processing: {lang}")
    try:
        for f in [ f for f in target_files if lang.capitalize() in str(f) ]:
            print(f"reading: {f}")
            # 言語名の指定
            local_settings[lang] = 1
            # 型名の指定
            for type in vars:
                if type in str(f):
                    local_settings[type] = 1
            #
            d = pd.read_csv(f, encoding='utf-8', sep = ",", on_bad_lines = 'skip') # Crucially, ...= skip
            df = pd.DataFrame(d, columns = vars)
            for var in [ var for var in (types + langs) if var != 'freq' ]:
                df[var] = local_settings[var]
            d_parts.append(df)
    except IndexError:
        pass
#
if verbose:
    d_parts

targe var names: ['form', 'spell', 'sound', 'freq', 'english', 'esperanto', 'french', 'german', 'icelandic', 'russian', 'swahili']
processing: english
reading: data-words/base-sound-English-r6e-originals.csv
reading: data-words/base-spell-English-r6e-originals.csv
processing: esperanto
reading: data-words/base-spell-Esperanto-r0-orginal.csv
processing: french
reading: data-words/base-spell-French-r0-originals.csv
processing: german
reading: data-words/base-sound-German-r1a-original.csv
reading: data-words/base-spell-German-r1a-originals.csv
processing: icelandic
reading: data-words/base-spell-Icelandic-r0-original.csv
processing: russian
reading: data-words/base-spell-Russian-r0-originals.csv
processing: swahili
reading: data-words/base-spell-Swahili-r0-orginal.csv

## データ統合

raw_df = pd.concat(d_parts)
raw_df

## 文字数の列を追加

raw_df['size'] = [ len(x) for x in raw_df[doc_type] ]
raw_df

## 言語名= language の列を追加

check = False
language_vals = [ ]
for i, row in raw_df.iterrows():
    if check:
        print(row)
    for j, lang in enumerate(langs):
        if check:
            print(f"{i}: {lang}")
        if row[lang] == 1:
            language_vals.append(lang)
if verbose:
    print(language_vals)
    len(language_vals)
#
raw_df['language'] = language_vals
raw_df

## 言語の選別

select_languages = True

selected_langs = re.split(r",\s*", "english, french, german, russian")
print(f"selected languages: {selected_langs}")

if select_languages:
    df_new = [ ]
    for lang in selected_langs:
        df_new.append(raw_df[raw_df[lang] == 1])
    raw_df = pd.concat(df_new)
#
raw_df

selected languages: ['english', 'french', 'german', 'russian']

## 文字数の分布
import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(1,1,1)

ax.hist(raw_df['size'], bins = 40)
ax.set_xlabel('length of doc')
ax.set_ylabel('freq')
plt.title(f"Length distribution for docs")
fig.show()

/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_52968/1088473461.py:12: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown
  fig.show()

## 長さで濾過

print(f"max doc size: {max_doc_size}")
original_size = len(raw_df)
raw_df = raw_df[raw_df['size'] < max_doc_size]
filtered_size = len(raw_df)
print(f"{original_size - filtered_size} cases removed")

max doc size: 12
293 cases removed

## 結果の検査 1

for lang in langs:
    print(raw_df[lang].value_counts())

english
1    8249
0    3523
Name: count, dtype: int64
esperanto
0    11772
Name: count, dtype: int64
french
0    10787
1      985
Name: count, dtype: int64
german
0    10207
1     1565
Name: count, dtype: int64
icelandic
0    11772
Name: count, dtype: int64
russian
0    10799
1      973
Name: count, dtype: int64
swahili
0    11772
Name: count, dtype: int64

## 結果の検査 2

for type in types:
    print(raw_df[type].value_counts())

spell
1    6883
0    4889
Name: count, dtype: int64
sound
1    9814
0    1958
Name: count, dtype: int64
freq
1                  10790
1                    966
1 не                   1
1 то время как         1
1 северу               1
1 него                 1
1 будет                1
1 образом              1
1 мышь                 1
Name: count, dtype: int64

## 統合: 割合補正を適用

eng_reduct_factor = 0.25
if balanced:
    eng_df = raw_df[raw_df['english'] == 1]
    non_eng_df = raw_df[raw_df['english'] == 0]
    eng_reduced_df = eng_df.sample(round(len(eng_df) * eng_reduct_factor))
    raw_df = pd.concat([eng_reduced_df, non_eng_df])
raw_df

## 結果の検査 3

for lang in langs:
    print(raw_df[lang].value_counts())

english
0    3523
1    2062
Name: count, dtype: int64
esperanto
0    5585
Name: count, dtype: int64
french
0    4600
1     985
Name: count, dtype: int64
german
0    4020
1    1565
Name: count, dtype: int64
icelandic
0    5585
Name: count, dtype: int64
russian
0    4612
1     973
Name: count, dtype: int64
swahili
0    5585
Name: count, dtype: int64

## 順序のランダマイズ

import sklearn.utils
raw_df = sklearn.utils.shuffle(raw_df)

## データ名の指定
df = raw_df[raw_df[doc_attr] == 1]
print(f"doc_attr: {doc_attr}")
df

doc_attr: spell

df[df['english'] == 1]

## ngram の追加

import sys
sys.path.append('..')

import re
import ngrams
import importlib
importlib.reload(ngrams)
import ngrams_skippy

bases = df[doc_type]

## 1gram 列の追加
#sep = r""
#unigrams = [ list(filter(lambda x: len(x) > 0, y)) for y in [ re.split(sep, z) for z in bases ] ]
unigrams = ngrams.gen_unigrams(bases, sep = r"", check = False)
if verbose:
    random.sample(unigrams, 5)
#
df['1gram'] = unigrams
#df.loc[:,'1gram'] = unigrams
df

/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_52968/1248262955.py:21: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['1gram'] = unigrams

## 2gram列の追加

bigrams = ngrams.gen_bigrams(bases, sep = r"", check = False)

## 包括的 2gram の作成
if ngram_is_inclusive:
    bigrams = [ [*b, *u] for b, u in zip(bigrams, unigrams) ]

if verbose:
    print(random.sample(bigrams, 3))

df['2gram'] = bigrams
if verbose:
    df

/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_52968/1480305306.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['2gram'] = bigrams

## 3gram列の追加

trigrams = ngrams.gen_trigrams(bases, sep = r"", check = False)

## 包括的 3gram の作成
if ngram_is_inclusive:
    trigrams = [ [ *t, *b ] for t, b in  zip(trigrams, bigrams) ]

if verbose:
    print(random.sample(trigrams, 3))

df['3gram'] = trigrams
if verbose:
    df

/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_52968/3715201492.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['3gram'] = trigrams

## skippy 2grams の生成

import sys
sys.path.append("..") # library path に一つ上の階層を追加

import ngrams_skippy

skippy_2grams = [ ngrams_skippy.generate_skippy_bigrams(x,
                                                         missing_mark = '…',
                                                         max_distance = max_distance_val, check = False)
                  for x in df['1gram'] ]

## 包括的 skippy 2-grams の生成
if ngram_is_inclusive:
    for i, b2 in enumerate(skippy_2grams):
        b2.extend(unigrams[i])
#
if verbose:
    random.sample(skippy_2grams, 3)

## skippy 2gram 列の追加

df['skippy2gram'] = skippy_2grams
df

/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_52968/3263801935.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['skippy2gram'] = skippy_2grams

## skippy 3grams の生成

import sys
sys.path.append("..") # library path に一つ上の階層を追加

import ngrams_skippy

skippy_3grams = [ ngrams_skippy.generate_skippy_trigrams(x,
                                                         missing_mark = '…',
                                                         max_distance = max_distance_val, check = False)
                  for x in df['1gram'] ]

## 包括的 skippy 3-grams の生成
if ngram_is_inclusive:
    for i, t2 in enumerate(skippy_3grams):
        t2.extend(skippy_2grams[i])
#
if verbose:
    random.sample(skippy_3grams, 3)

## skippy 3gram 列の追加

df['skippy3gram'] = skippy_3grams
df

/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_52968/1159231133.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['skippy3gram'] = skippy_3grams

## LDA 構築の基になる document-term matrix (dtm) を構築

from gensim.corpora.dictionary import Dictionary

bots = df[term_type]
diction = Dictionary(bots)

## 結果の確認
print(diction)

Dictionary<1189 unique tokens: ['а', 'ас', 'о', 'с', 'ст']...>

## diction の濾過

import copy
diction_copy = copy.deepcopy(diction)

## filter適用: 実は諸刃の刃で，token数が少ない時には適用しない方が良い
print(f"min freq filter: {term_min_freq}")
print(f"abuse filter: {term_abuse_threshold}")

apply_filter = True
if apply_filter:
    diction_copy.filter_extremes(no_below = term_min_freq, no_above = term_abuse_threshold)

## check
print(diction_copy)

min freq filter: 2
abuse filter: 0.05
Dictionary<862 unique tokens: ['ас', 'ст', 'то', 'ч', 'ча']...>

## Corpus (gensim の用語では corpus) の構築

corpus = [ diction.doc2bow(bot) for bot in bots ]

## check
check = True
if verbose:
    sample_n = 5
    print(random.sample(corpus, sample_n))
#
print(f"Number of documents: {len(corpus)}")

Number of documents: 3765

## LDA モデルの構築

from gensim.models import LdaModel
#from tqdm import tqdm

## LDAモデル
print(f"Building LDA model with n_topics: {n_topics}")
lda = LdaModel(corpus, id2word = diction, num_topics = n_topics, alpha = 0.01)
#
print(lda) # print(..)しないと中身が見れない

Building LDA model with n_topics: 15
LdaModel<num_terms=1189, num_topics=15, decay=0.5, chunksize=2000>

%%capture --no-display

## LDA のtopic ごとに，関連度の高い term を表示

import pandas as pd

n_terms = 20 # topic ごとに表示する term 数の指定
topic_dfs = [ ]
for topic in range(n_topics):
    terms = [ ]
    for i, prob in lda.get_topic_terms(topic, topn = n_terms):
        terms.append(diction.id2token[ int(i) ])
    #
    topic_dfs.append(pd.DataFrame([terms], index = [ f'topic {topic+1}' ]))
#
topic_term_df = pd.concat(topic_dfs)

## Table で表示
topic_term_df.T

%%capture --no-display

## pyLDAvis を使った結果 LDA の可視化: 階層クラスタリングより詳しい

import pyLDAvis

#installed_version = sys.version
installed_version = pyLDAvis.__version__

print(f"installed_version: {installed_version}")

if float(installed_version[:3]) > 3.1:
    import pyLDAvis.gensim_models as gensimvis
else:
    import pyLDAvis.gensim as gensimvis
#
pyLDAvis.enable_notebook()

#
lda_used     = lda
corpus_used  = corpus
diction_used = diction

## 実行パラメター
use_tSNE = False
if use_tSNE:
    vis = gensimvis.prepare(lda_used, corpus_used, diction_used, mds = 'tsne',
                            n_jobs = 1, sort_topics = False)
else:
    vis = gensimvis.prepare(lda_used, corpus_used, diction_used,
                            n_jobs = 1, sort_topics = False)
#
pyLDAvis.display(vis)

## 結果について
## topic を表わす円の重なりが多いならn_topics が多過ぎる可能性がある．
## ただし2Dで重なっていても，3Dなら重なっていない可能性もある

## LDA がD に対して生成した topics の弁別性を確認

## 得られたtopics を確認
topic_dist = lda.get_topics()
if verbose:
    topic_dist

## 検査 1: topic ごとに分布の和を取る

print(topic_dist.sum(axis = 1))

[1.0000001  1.         1.0000002  1.         1.         1.
 1.0000001  1.         0.9999999  1.         1.         1.0000001
 1.0000001  1.0000001  0.99999994]

## 検査 2: 総和を求める: n_topics にほぼ等しいなら正常

print(topic_dist.sum())

15.0

## term エンコード値の分布を確認

import matplotlib.pyplot as plt

plt.figure(figsize = (4,5))

sampling_rate = 0.3
df_size = len(topic_dist)
sample_n = round(df_size * sampling_rate)
topic_sampled = random.sample(list(topic_dist), sample_n)

T = sorted([ sorted(x, reverse = True) for x in topic_sampled ])

plt.plot(T, range(len(T)))
plt.title("Distribution of sorted values ({sample_n} samples) for topic/term encoding")
plt.show()

## tSNE を使った topics のグループ化 (3D)

from sklearn.manifold import TSNE
import numpy as np

## tSNE のパラメターを設定
## n_components は射影先の空間の次元: n_components = 3 なら3次元空間に射影
## perplexity は結合の強さを表わす指数で，値に拠って結果が代わるので，色々な値を試すと良い

#perplexity_val = 10 # 大き過ぎると良くない
top_perplexity_reduct_rate = 0.3
perplexity_val = round(len(topic_dist) * top_perplexity_reduct_rate)
topic_tSNE_3d = TSNE(n_components = 3, random_state = 0, perplexity = perplexity_val, n_iter = 1000)

## データに適用
top_tSNE_3d_fitted = topic_tSNE_3d.fit_transform(np.array(topic_dist))

## Plotlyを使って tSNE の結果の可視化 (3D)

#import plotly.express as pex
import plotly.graph_objects as go
import numpy as np

top_tSNE = top_tSNE_3d_fitted
fig = go.Figure(data = [go.Scatter3d(x = top_tSNE[:,0], y = top_tSNE[:,1], z = top_tSNE[:,2],
                                     mode = 'markers')])

## 3D 散布図にラベルを追加する処理は未実装
title_val = f"3D tSNE view for LDA (#topics: {n_topics}, doc: {doc_type}, term: {term_size} {term_type})"
fig.update_layout(autosize = False,
                  width = 600, height = 600, title = title_val)
fig.show()

## 構築した LDA モデルを使って文(書)を分類する

## .get_document_topics(..) は　minimu_probability = 0としないと
## topic の値が小さい場合に値を返さないので，

## パラメター

ntopics = n_topics # LDA の構築の最に指定した値を使う

check      = False
encoding = [ ]
for i, row in df.iterrows():
    if check:
        print(f"row: {row}")
    doc = row[doc_type]
    bot = row[term_type]
    ## get_document_topics(..) では　minimu_probability = 0 としないと
    ## 値が十分に大きな topics に関してだけ値が取れる
    enc = lda.get_document_topics(diction.doc2bow(bot), minimum_probability = 0)
    if check:
        print(f"enc: {enc}")
    encoding.append(enc)
#
len(encoding)

3765

## enc 列の追加

#df['enc'] = np.array(encoding) # This flattens arrays
#df['enc'] = list(encoding) # ineffective
df['enc'] = [ list(map(lambda x: x[1], y)) for y in encoding ]
if verbose:
    df['enc']

/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_52968/1047258704.py:5: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

## エンコーディングのstd の分布を見る

from scipy.stats import tstd
from matplotlib import pyplot as plt

plt.figure(figsize = (6,4))

std_data = [ tstd(x) for x in df['enc'] ]
plt.hist(std_data)

plt.title("Distribution of standard deviations")
plt.show()

## doc のエンコーディング

## 一様分布の事例を除外

from scipy.stats import tstd # standard deviation の計算用

print(f"{len(df)} instances before filtering")

check = False

doc_enc = df['enc']
max_std = max([ tstd(x) for x in doc_enc])
if check: print(f"std max: {max_std}")

min_std = min([ tstd(x) for x in doc_enc])
if check: print(f"std min: {min_std}")

first_min_std = list(sorted(set([ tstd(x) for x in doc_enc])))[-0]
print(f"std 1st min: {first_min_std}")

second_min_std = list(sorted(set([ tstd(x) for x in doc_enc])))[-1]
print(f"std 2nd min: {second_min_std}")

3765 instances before filtering
std 1st min: 0.0
std 2nd min: 0.25636767221964024

## df_filtered の定義

## 閾値は2番目に小さい値より小さく最小値よりは大きな値であるべき
std_threshold = second_min_std / 4 # 穏健な値を得るために4で割った
print(f"std_threshold: {std_threshold}")

## Rっぽい次のコードは通らない
#df_filtered = df[ df['encoding'] > std_threshold ]

## 通るのは次のコード: Creating a list of True/False and apply it to DataFrame 
std_tested = [ False if tstd(x) < std_threshold else True for x in df['enc'] ]
df_filtered = df[ std_tested ]
#
print(f"{len(df_filtered)} instances after filtering ({len(df) - len(df_filtered)} instances removed)")

std_threshold: 0.06409191805491006
3764 instances after filtering (1 instances removed)

## doc エンコード値の分布を確認

sample_n = 50
E = sorted([ sorted(x, reverse = True) for x in df_filtered['enc'].sample(sample_n) ])

plt.figure(figsize = (5,5))
plt.plot(E, range(len(E)))
plt.title(f"Distribution of sorted encoding values for sampled {sample_n} docs")
plt.show()

len(df_filtered['language'])

3764

## tSNE 用の事例サンプリング = tSNE_df の定義

tSNE_sampling = True
tSNE_sampling_rate = 0.33
if tSNE_sampling:
    tSNE_df_original = df_filtered.copy()
    sample_n = round(len(tSNE_df_original) * tSNE_sampling_rate)
    tSNE_df = tSNE_df_original.sample(sample_n)
    print(f"tSNE_df has {len(tSNE_df)} rows after sampling")
else:
    tSNE_df = df_filtered

tSNE_df has 1242 rows after sampling

tSNE_df.columns

Index(['form', 'spell', 'sound', 'freq', 'english', 'esperanto', 'french',
       'german', 'icelandic', 'russian', 'swahili', 'size', 'language',
       '1gram', '2gram', '3gram', 'skippy2gram', 'skippy3gram', 'enc'],
      dtype='object')

## tSNE の結果の可視化: Plotly を使った 3D 描画

import numpy as np
from sklearn.manifold import TSNE as tSNE

import plotly.express as pex
import plotly.graph_objects as go
import matplotlib.pyplot as plt

## tSNE のパラメターを設定

perplexity_max_val = round(len(tSNE_df)/4)

for perplexity_val in range(5, perplexity_max_val, 30):
    
    ## tSNE 事例の生成
    tSNE_3d_varied = tSNE(n_components = 3, random_state = 0, perplexity = perplexity_val, n_iter = 1000)

    ## データに適用
    doc_enc = np.array(list(tSNE_df['enc']))
    doc_tSNE_3d_varied = tSNE_3d_varied.fit_transform(doc_enc)

    T = zip(doc_tSNE_3d_varied[:,0], doc_tSNE_3d_varied[:,1], doc_tSNE_3d_varied[:,2],
            tSNE_df['language']) # zip(..)が必要
    df = pd.DataFrame(T, columns = ['D1', 'D2', 'D3', 'language'])

    ## 作図
    fig = go.Figure()
    for lang in np.unique(df['language']):
        part = df[df['language'] == lang]
        fig.add_trace(
            go.Scatter3d(
                x = part['D1'], y = part['D2'], z = part['D3'],
                name = lang, mode = 'markers', marker = dict(size = 6),
                showlegend = True
            )
        )

    title_val = f"tSNE 3D map (ppl: {perplexity_val}) of {doc_attr}s encoded by LDA ({n_topics} topics; term: {term_type})"
    fig.update_layout(title = dict(text = title_val),
                      autosize = False, width = 600, height = 600,)
    fig.show()

## 階層クラスタリングのための事例のサンプリング

hc_sampling_rate = 0.1 # 大きくし過ぎると図が見にくい
df_size = len(tSNE_df)
hc_sample_n = round(df_size * hc_sampling_rate)
hc_df = tSNE_df.sample(hc_sample_n)
##
print(f"{hc_sample_n} rows are sampled")
hc_df['language'].value_counts()

124 rows are sampled

language
french     38
english    35
russian    35
german     16
Name: count, dtype: int64

## doc 階層クラスタリングの実行

import numpy as np
import plotly
import matplotlib.pyplot as plt

from scipy.cluster.hierarchy import dendrogram, linkage

## 距離行列の生成
Enc = list(hc_df['enc'])
linkage = linkage(Enc, method = 'ward', metric = 'euclidean')

## 描画サイズの指定
plt.figure(figsize = (5, round(len(hc_df) * 0.15))) # This needs to be run here, before dendrogram construction.

## 事例ラベルの生成
label_vals = [ x[:max_doc_size] for x in list(hc_df[doc_type]) ] # truncate doc keys

## 樹状分岐図の作成
dendrogram(linkage, orientation = 'left', labels = label_vals, leaf_font_size = 7)

## 描画
plt.title(f"Hierarchical clustering of (sampled) {len(hc_df)} (= {100 * hc_sampling_rate}%) {doc_attr}s as docs\n \
    encoded via LDA ({n_topics} topics) with {term_type} as terms")

## ラベルに language に対応する色を付ける
lang_colors = { lang_name : i for i, lang_name in enumerate(np.unique(hc_df['language'])) }
ax = plt.gca()
for ticker in ax.get_ymajorticklabels():
    form = ticker.get_text()
    row = hc_df.loc[hc_df[doc_type] == form]
    #lang = row['language']
    lang = row['language'].to_string().split()[-1] # trick
    try:
        lang_id = lang_colors[lang]
    except (TypeError, KeyError):
        print(f"color encoding error at: {lang}")
    #
    ticker.set_color(plotly.colors.qualitative.Plotly[lang_id]) # id の基数調整
#
plt.show()

## tSNE の結果の可視化 (2D)

#import seaborn as sns
import numpy as np
import plotly
import plotly.express as pex
import matplotlib.pyplot as plt
from adjustText import adjust_text

## tSNE 事例の生成
perplexity_selected = 250
tSNE_3d = tSNE(n_components = 3, random_state = 0, perplexity = perplexity_selected, n_iter = 1000)

## データに適用
doc_enc = np.array(list(tSNE_df['enc']))
doc_tSNE_3d = tSNE_3d.fit_transform(doc_enc)

T = zip(doc_tSNE_3d[:,0], doc_tSNE_3d[:,1], doc_tSNE_3d[:,2],
        tSNE_df['language']) # zip(..)が必要
df = pd.DataFrame(T, columns = ['D1', 'D2', 'D3', 'language'])

## 描画
plt.figure(figsize = (5, 5))
plt.set_colors = pex.colors.qualitative.Plotly
    
for r in [ np.roll([0,1,2], -i) for i in range(0,3) ]:
    if check:
        print(r)
    X, Y = df.iloc[:,r[0]], df.iloc[:,r[1]]
    gmax = max(X.max(), Y.max())
    gmin = min(X.min(), Y.min())
    plt.xlim(gmin, gmax)
    plt.ylim(gmin, gmax)

    colormap = pex.colors.qualitative.Plotly
    lang_list = list(np.unique(tSNE_df['language']))
    cmapped = [ colormap[lang_list.index(lang)] for lang in df['language'] ]
    scatter = plt.scatter(X, Y, s = 40, c = cmapped, edgecolors = 'w')

    ## 文字を表示する事例のサンプリング
    lab_sampling_rate = 0.02
    lab_sample_n = round(len(tSNE_df) * lab_sampling_rate)
    sampled_keys = [ doc[:max_doc_size] for doc in random.sample(list(tSNE_df[doc_type]), lab_sample_n) ]

    ## labels の生成
    texts = [ ]
    for x, y, s in zip(X, Y, sampled_keys):
        texts.append(plt.text(x, y, s, size = 9, color = 'blue'))

    ## label に repel を追加: adjustText package の導入が必要
    adjust_text(texts, force_points = 0.2, force_text = 0.2,
                expand_points = (1, 1), expand_text = (1, 1),
                arrowprops = dict(arrowstyle = "-", color = 'black', lw = 0.5))
    #

    plt.title(f"tSNE (ppl: {perplexity_selected}) 2D map of {len(tSNE_df)} {doc_attr}s via LDA ({term_type}; {n_topics} topics)")
    #plt.legend(np.unique(cmapped))
    plt.show()

	form	spell	sound	freq	english	esperanto	french	german	icelandic	russian	swahili	size	language	1gram	2gram	3gram	skippy2gram
290	часто	1	0	1	0	0	0	0	0	1	0	5	russian	[ч, а, с, т, о]	[ча, ас, ст, то, ч, а, с, т, о]	[час, аст, сто, ча, ас, ст, то, ч, а, с, т, о]	[ча, ч…с, ч…т, ч…о, ас, а…т, а…о, ст, с…о, то,...
3204	risk	1	1	1.0	1	0	0	0	0	0	0	4	english	[r, i, s, k]	[ri, is, sk, r, i, s, k]	[ris, isk, ri, is, sk, r, i, s, k]	[ri, r…s, r…k, is, i…k, sk, r, i, s, k]
1358	famous	1	1	1.0	1	0	0	0	0	0	0	6	english	[f, a, m, o, u, s]	[fa, am, mo, ou, us, f, a, m, o, u, s]	[fam, amo, mou, ous, fa, am, mo, ou, us, f, a,...	[fa, f…m, f…o, f…u, f…s, am, a…o, a…u, a…s, mo...
1357	family	1	1	1.0	1	0	0	0	0	0	0	6	english	[f, a, m, i, l, y]	[fa, am, mi, il, ly, f, a, m, i, l, y]	[fam, ami, mil, ily, fa, am, mi, il, ly, f, a,...	[fa, f…m, f…i, f…l, f…y, am, a…i, a…l, a…y, mi...
2196	lived	1	1	1.0	1	0	0	0	0	0	0	5	english	[l, i, v, e, d]	[li, iv, ve, ed, l, i, v, e, d]	[liv, ive, ved, li, iv, ve, ed, l, i, v, e, d]	[li, l…v, l…e, l…d, iv, i…e, i…d, ve, v…d, ed,...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
723	sklave	1	1	1	0	0	0	1	0	0	0	6	german	[s, k, l, a, v, e]	[sk, kl, la, av, ve, s, k, l, a, v, e]	[skl, kla, lav, ave, sk, kl, la, av, ve, s, k,...	[sk, s…l, s…a, s…v, s…e, kl, k…a, k…v, k…e, la...
0	comme	1	0	1.0	0	0	1	0	0	0	0	5	french	[c, o, m, m, e]	[co, om, mm, me, c, o, m, m, e]	[com, omm, mme, co, om, mm, me, c, o, m, m, e]	[co, c…m, c…e, om, o…m, o…e, mm, m…e, me, c, o...
552	caughey	1	1	1.0	1	0	0	0	0	0	0	7	english	[c, a, u, g, h, e, y]	[ca, au, ug, gh, he, ey, c, a, u, g, h, e, y]	[cau, aug, ugh, ghe, hey, ca, au, ug, gh, he, ...	[ca, c…u, c…g, c…h, c…e, c…y, au, a…g, a…h, a…...
143	движение	1	0	1	0	0	0	0	0	1	0	8	russian	[д, в, и, ж, е, н, и, е]	[дв, ви, иж, же, ен, ни, ие, д, в, и, ж, е, н,...	[дви, виж, иже, жен, ени, ние, дв, ви, иж, же,...	[дв, д…и, д…ж, д…е, д…н, ви, в…ж, в…е, в…н, в…...
3848	thumb	1	1	1.0	1	0	0	0	0	0	0	5	english	[t, h, u, m, b]	[th, hu, um, mb, t, h, u, m, b]	[thu, hum, umb, th, hu, um, mb, t, h, u, m, b]	[th, t…u, t…m, t…b, hu, h…m, h…b, um, u…b, mb,...

	form	spell	sound	freq	english	esperanto	french	german	icelandic	russian	swahili	size	language	1gram	2gram	3gram	skippy2gram	skippy3gram
290	часто	1	0	1	0	0	0	0	0	1	0	5	russian	[ч, а, с, т, о]	[ча, ас, ст, то, ч, а, с, т, о]	[час, аст, сто, ча, ас, ст, то, ч, а, с, т, о]	[ча, ч…с, ч…т, ч…о, ас, а…т, а…о, ст, с…о, то,...	[час, ча…т, ча…о, ч…ст, ч…с…о, ч…то, аст, ас…о...
3204	risk	1	1	1.0	1	0	0	0	0	0	0	4	english	[r, i, s, k]	[ri, is, sk, r, i, s, k]	[ris, isk, ri, is, sk, r, i, s, k]	[ri, r…s, r…k, is, i…k, sk, r, i, s, k]	[ris, ri…k, r…sk, isk, ri, r…s, r…k, is, i…k, ...
1358	famous	1	1	1.0	1	0	0	0	0	0	0	6	english	[f, a, m, o, u, s]	[fa, am, mo, ou, us, f, a, m, o, u, s]	[fam, amo, mou, ous, fa, am, mo, ou, us, f, a,...	[fa, f…m, f…o, f…u, f…s, am, a…o, a…u, a…s, mo...	[fam, fa…o, fa…u, fa…s, f…mo, f…m…u, f…m…s, f…...
1357	family	1	1	1.0	1	0	0	0	0	0	0	6	english	[f, a, m, i, l, y]	[fa, am, mi, il, ly, f, a, m, i, l, y]	[fam, ami, mil, ily, fa, am, mi, il, ly, f, a,...	[fa, f…m, f…i, f…l, f…y, am, a…i, a…l, a…y, mi...	[fam, fa…i, fa…l, fa…y, f…mi, f…m…l, f…m…y, f…...
2196	lived	1	1	1.0	1	0	0	0	0	0	0	5	english	[l, i, v, e, d]	[li, iv, ve, ed, l, i, v, e, d]	[liv, ive, ved, li, iv, ve, ed, l, i, v, e, d]	[li, l…v, l…e, l…d, iv, i…e, i…d, ve, v…d, ed,...	[liv, li…e, li…d, l…ve, l…v…d, l…ed, ive, iv…d...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
723	sklave	1	1	1	0	0	0	1	0	0	0	6	german	[s, k, l, a, v, e]	[sk, kl, la, av, ve, s, k, l, a, v, e]	[skl, kla, lav, ave, sk, kl, la, av, ve, s, k,...	[sk, s…l, s…a, s…v, s…e, kl, k…a, k…v, k…e, la...	[skl, sk…a, sk…v, sk…e, s…la, s…l…v, s…l…e, s…...
0	comme	1	0	1.0	0	0	1	0	0	0	0	5	french	[c, o, m, m, e]	[co, om, mm, me, c, o, m, m, e]	[com, omm, mme, co, om, mm, me, c, o, m, m, e]	[co, c…m, c…e, om, o…m, o…e, mm, m…e, me, c, o...	[com, co…m, co…e, c…mm, c…m…e, c…me, omm, om…e...
552	caughey	1	1	1.0	1	0	0	0	0	0	0	7	english	[c, a, u, g, h, e, y]	[ca, au, ug, gh, he, ey, c, a, u, g, h, e, y]	[cau, aug, ugh, ghe, hey, ca, au, ug, gh, he, ...	[ca, c…u, c…g, c…h, c…e, c…y, au, a…g, a…h, a…...	[cau, ca…g, ca…h, ca…e, ca…y, c…ug, c…u…h, c…u...
143	движение	1	0	1	0	0	0	0	0	1	0	8	russian	[д, в, и, ж, е, н, и, е]	[дв, ви, иж, же, ен, ни, ие, д, в, и, ж, е, н,...	[дви, виж, иже, жен, ени, ние, дв, ви, иж, же,...	[дв, д…и, д…ж, д…е, д…н, ви, в…ж, в…е, в…н, в…...	[дви, дв…ж, дв…е, дв…н, дв…и, д…иж, д…и…е, д…и...
3848	thumb	1	1	1.0	1	0	0	0	0	0	0	5	english	[t, h, u, m, b]	[th, hu, um, mb, t, h, u, m, b]	[thu, hum, umb, th, hu, um, mb, t, h, u, m, b]	[th, t…u, t…m, t…b, hu, h…m, h…b, um, u…b, mb,...	[thu, th…m, th…b, t…um, t…u…b, t…mb, hum, hu…b...

	form	spell	sound	freq	english	esperanto	french	german	icelandic	russian	swahili
0	æbəhəlimə	0	1	1	1	0	0	0	0	0	0
1	ædmaɪə	0	1	1	1	0	0	0	0	0	0
2	ædmɪʃən	0	1	1	1	0	0	0	0	0	0
3	ædvæntɪdʒ	0	1	1	1	0	0	0	0	0	0
4	ædvaɪs	0	1	1	1	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...
703	zaidi	1	0	1	0	0	0	0	0	0	1
704	ziara	1	0	1	0	0	0	0	0	0	1
705	zima	1	0	1	0	0	0	0	0	0	1
706	ziwa	1	0	1	0	0	0	0	0	0	1
707	zoezi	1	0	1	0	0	0	0	0	0	1

	form	spell	sound	freq	english	esperanto	french	german	icelandic	russian	swahili	size	language
1575	lɪst	0	1	1	1	0	0	0	0	0	0	4	english
2440	stænd	0	1	1	1	0	0	0	0	0	0	5	english
2812	places	1	1	1.0	1	0	0	0	0	0	0	6	english
971	hoʊm	0	1	1	1	0	0	0	0	0	0	4	english
1869	niz	0	1	1	1	0	0	0	0	0	0	3	english
...	...	...	...	...	...	...	...	...	...	...	...	...	...
992	соль	1	0	1	0	0	0	0	0	1	0	4	russian
993	нос	1	0	1	0	0	0	0	0	1	0	3	russian
995	гнев	1	0	1	0	0	0	0	0	1	0	4	russian
996	претензии	1	0	1	0	0	0	0	0	1	0	9	russian
997	континент	1	0	1	0	0	0	0	0	1	0	9	russian

	topic 1	topic 2	topic 3	topic 4	topic 5	topic 6	topic 7	topic 8	topic 9	topic 10	topic 11	topic 12	topic 13	topic 14	topic 15
0	r	о	a	o	о	h	i	e	l	l	e	e	e	а	е
1	o	т	t	t	е	c	e	s	o	е	n	c	r	о	т
2	e	л	u	a	н	ch	n	i	e	о	g	o	a	т	с
3	t	и	n	u	а	e	t	r	s	i	b	r	s	и	н
4	or	р	c	r	р	r	a	n	i	л	i	е	n	р	о
5	f	t	e	s	г	a	r	t	a	к	en	p	er	ь	и
6	b	я	s	e	и	t	d	u	r	e	r	h	l	к	ь
7	i	от	an	i	в	sc	m	o	t	il	c	л	t	д	м
8	d	ь	at	ou	д	o	u	p	le	le	s	n	p	с	р
9	u	ро	l	ut	м	щ	o	re	n	р	ge	l	g	н	п
10	rd	в	b	c	з	l	s	l	m	н	h	a	c	ть	ст
11	er	ю	о	h	го	е	l	a	u	с	ch	i	o	п	й
12	n	a	p	to	й	ha	h	c	b	c	к	t	en	в	ы
13	re	с	un	n	e	s	f	en	h	ло	eb	k	h	у	а
14	w	к	i	re	ра	m	in	m	d	ll	u	о	v	л	л
15	c	а	w	l	п	he	g	se	is	t	a	в	d	ат	ый
16	s	ть	f	au	к	n	c	in	al	ол	be	oc	i	з	ть
17	л	д	y	m	но	щи	en	d	y	и	he	д	u	ра	я
18	h	s	h	so	ой	и	te	g	la	ко	о	b	se	ч	ме
19	br	сл	r	ai	во	î	ie	er	li	ic	ri	re	ve	ка	ес